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Published: 25 Jul 2023, Last Modified: 17 Sept 2024Accepted by TMLREveryoneRevisionsBibTeXCC BY 4.0 Abstract: Existing combinatorial search methods are often complex and require some level of expertise. This work introduces a simple and efficient deep learning method for solving combinatorial problems with a predefined goal, represented by Rubik's Cube. We demonstrate that, for such problems, trai

Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Recommendations. Open Directory. Open API. Open Source. Abstract: How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multi

Published: 20 Dec 2019, Last Modified: 22 Jun 2025ICLR 2020 Conference Blind SubmissionReaders: Everyone TL;DR: A text encoder trained to distinguish real input tokens from plausible fakes efficiently learns effective language representations. Abstract: Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to

Published: 20 Dec 2019, Last Modified: 22 Jun 2025ICLR 2020 Conference Blind SubmissionReaders: Everyone TL;DR: We train a neural network to compute function integrals, and to solve complex differential equations. Abstract: Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper,

Published: 21 Dec 2018, Last Modified: 05 May 2023ICLR 2019 Conference Blind SubmissionReaders: Everyone Abstract: We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces (N-balls for short). Inclusion relations among N-balls implicitly encode subordinate relations among categories. The simil

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Published: 06 Feb 2017, Last Modified: 12 Oct 2025ICLR 2017 PosterReaders: Everyone Abstract: Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has p

Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Recommendations. Open Directory. Open API. Open Source. Abstract: We design a new policy, called a nearest neighbor policy, that does not require any optimization for simple, low-dimensional continuous control tasks. As this policy does not require any optimization, it allows us to investigate the underlying difficulty of a task

15 Feb 2018 (modified: 12 Oct 2025)ICLR 2018 Conference Blind SubmissionReaders: Everyone Abstract: Deep neural networks have led to a series of breakthroughs, dramatically improving the state-of-the-art in many domains. The techniques driving these advances, however, lack a formal method to account for model uncertainty. While the Bayesian approach to learning provides a solid theoretical framewo

Published: 06 Feb 2017, Last Modified: 12 Oct 2025ICLR 2017 OralReaders: Everyone Abstract: Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural netwo

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